Semi-Supervised Learning to Classify Evaluative Expressions from Labeled and Unlabeled Texts(<Special Section>Knowledge, Information and Creativity Support System)
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概要
- 論文の詳細を見る
In this paper, we present a method to automatically acquire a large-scale vocabulary of evaluative expressions from a large corpus of blogs. For the purpose, this paper presents a semi-supervised method for classifying evaluative expressions, that is, tuples of subjects, their attributes, and evaluative words, that indicate either favorable or unfavorable opinions towards a specific subject. Due to its characteristics, our semi-supervised method can classify evaluative expressions in a corpus by their polarities, starting from a very small set of seed training examples and using contextual information in the sentences the expressions belong to. Our experimental results with real Weblog data as our corpus show that this bootstrapping approach can improve the accuracy of methods for classifying favorable and unfavorable opinions. We also show that a reasonable amount of evaluative expressions can be really acquired.
- 社団法人電子情報通信学会の論文
- 2007-10-01
著者
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SUZUKI Yasuhiro
Department of Information Processing, Tokyo Institute of Technology
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Suzuki Yasuhiro
Department Of Computational Intelligence And Systems Science Interdisciplinary Graduate School Of Sc
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Okumura Manabu
Precision And Intelligence Laboratory Tokyo Institute Of Technology
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TAKAMURA HIROYA
Precision and Intelligence Laboratory, Tokyo Institute of Technology
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Takamura Hiroya
Precision And Intelligence Laboratory Tokyo Institute Of Technology
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Suzuki Yasuhiro
Department Of Breast And Endocrine Surgery Tokai University School Of Medicine
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Okumura Manabu
Tokyo Inst. Technol.
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